AI to help identify breast cancer risk and reduce unnecessary surgeries

Breast cancer

Breast cancer accounts for 40,000 deaths annually in the U.S.

Early detection is crucial for cancer sufferers, with mammograms being the best test available for detection. Generally if that biopsy is found to be abnormal a patient will undergo surgery to remove the lesion, but 90 percent of the time these lesions are found to be benign rendering the surgical procedure unnecessary.

Two authors disclosed having a patent in process with Massachusetts General Hospital and the Massachusetts Institute of Technology, and one author disclosed ties to GE Healthcare.

A machine learning tool can help identify which high-risk breast lesions are likely to become cancerous, reports a study from Massachusetts General Hospital/Harvard Medical School.

High-risk breast lesions - biopsy-diagnosed lesions that carry an increased risk of becoming cancerous - are most commonly surgically removed.

"Because diagnostic tools are so inexact, there is an understandable tendency for doctors to over-screen for breast cancer", says Regina Barzilay, MIT's Delta Electronics Professor of Electrical Engineering and Computer Science and a breast cancer survivor herself. The team trained the machine learning model on patients with high-risk lesions confirmed via biopsy who had either had surgery or two-year imaging after the biopsy and high-risk determination. "I believe we can capitalize on machine learning to inform clinical decision making and ultimately improve patient care". That's the finding of a new study published online in the journal Radiology. Of the 1006 high-risk lesions identified, 115 (11%) were upgraded to cancer.

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"There are different types of high-risk lesions", Dr. With "less immediate" cases, surgery may be deemed unnecessary and follow up imaging or other treatments may be found to be the preferred course of action - but only if there is a reliable way of differentiating between the lesions.

Researchers studied the use of a machine learning tool to identify high-risk lesions that are at low risk for upgrade to cancer.

Machine learning could drastically improve medical treatment for breast lesions.

Lehman says radiologists want to be more targeted and precise in their recommendations to their patients.

"In the past we might have recommended that all high-risk lesions be surgically excised", says Constance Lehman, one of the collaborators on the project.

The model, in this case, analyzed traditional risk factors, like lesion histology and patient age, along with a few more novel features like keywords in biopsy pathology reports.

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